a26699c5b5
Isolate projects to avoid transitive dependency Add parameter for passing devices Incorporate further reviews Update all dependencies
67 lines
2.7 KiB
Markdown
67 lines
2.7 KiB
Markdown
JuliaStream.jl
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==============
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This is an implementation of BabelStream in Julia which contains the following variants:
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* `PlainStream.jl` - Single threaded `for`
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* `ThreadedStream.jl` - Threaded implementation with `Threads.@threads` macros
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* `DistributedStream.jl` - Process based parallelism with `@distributed` macros
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* `CUDAStream.jl` - Direct port of BabelStream's native CUDA implementation using [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl)
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* `AMDGPUStream.jl` - Direct port of BabelStream's native HIP implementation using [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl)
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* `oneAPIStream.jl` - Direct port of BabelStream's native SYCL implementation using [oneAPI.jl](https://github.com/JuliaGPU/oneAPI.jl)
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* `KernelAbstractions.jl` - Direct port of miniBUDE's native CUDA implementation using [KernelAbstractions.jl](https://github.com/JuliaGPU/KernelAbstractions.jl)
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### Build & Run
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Prerequisites
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* Julia >= 1.6+
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A set of reduced dependency projects are available for the following backend and implementations:
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* `AMDGPU` supports:
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- `AMDGPUStream.jl`
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* `CUDA` supports:
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- `CUDAStream.jl`
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* `oneAPI` supports:
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- `oneAPIStream.jl`
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* `KernelAbstractions` supports:
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- `KernelAbstractionsStream.jl`
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* `Threaded` supports:
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- `PlainStream.jl`
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- `ThreadedStream.jl`
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- `DistributedStream.jl`
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With Julia on path, run your selected benchmark with:
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```shell
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> cd JuliaStream.jl
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> julia --project=<BACKEND> -e 'import Pkg; Pkg.instantiate()' # only required on first run
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> julia --project=<BACKEND> src/<IMPL>Stream.jl
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```
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For example. to run the CUDA implementation:
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```shell
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> cd JuliaStream.jl
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> julia --project=CUDA -e 'import Pkg; Pkg.instantiate()'
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> julia --project=CUDA src/CUDAStream.jl
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```
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**Important:**
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* Julia is 1-indexed, so N >= 1 in `--device N`.
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* Thread count for `ThreadedStream` must be set via the `JULIA_NUM_THREADS` environment variable (e.g `export JULIA_NUM_THREADS=$(nproc)`) otherwise it defaults to 1.
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* Worker count for `DistributedStream` is set with `-p <N>` as per the [documentation](https://docs.julialang.org/en/v1/manual/distributed-computing).
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* Certain implementations such as CUDA and AMDGPU will do hardware detection at runtime and may download and/or compile further software packages for the platform.
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***
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Alternatively, the top-level project `Project.toml` contains all dependencies needed to run all implementations in `src`.
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There may be instances where some packages are locked to an older version because of transitive dependency requirements.
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To run the benchmark using the top-level project, run the benchmark with:
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```shell
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> cd JuliaStream.jl
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> julia --project -e 'import Pkg; Pkg.instantiate()'
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> julia --project src/<IMPL>Stream.jl
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``` |